{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pickle as pkl\n",
    "import numpy as np\n",
    "import spectral\n",
    "import pandas as pd\n",
    "import scipy.io as io\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the ground truth image\n",
    "==========================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "DATA_PATH = os.path.join(os.getcwd(),\"Data\")\n",
    "output_image = io.loadmat(os.path.join(DATA_PATH, 'Indian_pines_gt.mat'))['indian_pines_gt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the target image\n",
    "======================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "height = output_image.shape[0]\n",
    "width = output_image.shape[1]\n",
    "targets = []\n",
    "for j in range(height):\n",
    "    for i in range(width):\n",
    "        if output_image[j][i]==0 :\n",
    "            continue\n",
    "        else :\n",
    "            targets.append(output_image[j][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polulation of target pixels of different classes:  [46, 1428, 830, 237, 483, 730, 28, 478, 20, 972, 2455, 593, 205, 1265, 386, 93]\n"
     ]
    }
   ],
   "source": [
    "unq, unq_idx = np.unique(targets, return_inverse=True)\n",
    "unq_cnt = np.bincount(unq_idx)\n",
    "count_mat = []\n",
    "for i in range(len(unq_cnt)):\n",
    "    count_mat.append(unq_cnt[i])\n",
    "print \"Polulation of target pixels of different classes: \", count_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "validation_scores = {'5x5': 86.19, '11x11':85.19, '21x21':97.31, '31x31':98.19, '37x37':99.56}\n",
    "CLASSES = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total = sum(validation_scores.values())\n",
    "credibility = {}\n",
    "for keys,value in validation_scores.items():\n",
    "    credibility[keys]=value/total "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = open('Predictions.pkl','rb')\n",
    "output_predictions = {}\n",
    "for i in range(5):\n",
    "    for keys, values in (pkl.load(f).iteritems()):\n",
    "        score = validation_scores[keys]\n",
    "        for a in range(len(values)):\n",
    "            for b in range(len(values)):\n",
    "                if isinstance(values[a][b],int):\n",
    "                    values[a][b] = np.zeros((16))       \n",
    "        output_predictions[keys] = np.asarray(values)*credibility[keys]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "final_matrix = sum(output_predictions.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0          1          2    3          4          5    6          7    8   \\\n",
      "0   100   0.000000   0.000000    0   0.000000   0.000000    0   0.627615    0   \n",
      "1     0  93.137255   1.807229    0   0.000000   0.000000    0   0.000000    0   \n",
      "2     0   0.630252  83.132530    0   0.000000   0.000000    0   0.000000    0   \n",
      "3     0   0.000000   0.240964  100   0.000000   0.410959    0   0.000000    0   \n",
      "4     0   0.070028   0.000000    0  95.031056   0.000000    0   0.209205    0   \n",
      "5     0   0.000000   0.120482    0   0.207039  99.589041    0   0.000000    0   \n",
      "6     0   0.000000   0.000000    0   0.000000   0.000000  100   0.000000    0   \n",
      "7     0   0.000000   0.000000    0   0.000000   0.000000    0  99.163180    0   \n",
      "8     0   0.000000   0.000000    0   0.000000   0.000000    0   0.000000  100   \n",
      "9     0   2.380952   0.843373    0   0.000000   0.000000    0   0.000000    0   \n",
      "10    0   1.610644   0.361446    0   0.000000   0.000000    0   0.000000    0   \n",
      "11    0   2.100840   7.108434    0   0.621118   0.000000    0   0.000000    0   \n",
      "12    0   0.000000   0.000000    0   0.000000   0.000000    0   0.000000    0   \n",
      "13    0   0.000000   0.000000    0   1.035197   0.000000    0   0.000000    0   \n",
      "14    0   0.070028   0.000000    0   0.000000   0.000000    0   0.000000    0   \n",
      "15    0   0.000000   0.000000    0   0.000000   0.000000    0   0.000000    0   \n",
      "\n",
      "           9          10         11   12         13         14   15  \n",
      "0    0.000000   0.122200   0.000000    0   0.000000   0.000000    0  \n",
      "1    0.925926   2.240326   0.000000    0   0.000000   0.000000    0  \n",
      "2    0.308642   0.448065   1.011804    0   0.000000   0.000000    0  \n",
      "3    0.000000   0.000000   0.000000    0   0.000000   0.000000    0  \n",
      "4    0.000000   0.162933   0.000000    0   0.316206   0.000000    0  \n",
      "5    0.000000   0.244399   0.000000    0   0.000000   0.000000    0  \n",
      "6    0.000000   0.000000   0.000000    0   0.000000   0.000000    0  \n",
      "7    0.000000   0.000000   0.000000    0   0.000000   0.000000    0  \n",
      "8    0.000000   0.000000   0.000000    0   0.079051   0.000000    0  \n",
      "9   91.563786   2.973523   0.000000    0   0.000000   0.000000    0  \n",
      "10   5.452675  86.965377   0.337268    0   0.000000   0.000000    0  \n",
      "11   1.131687   4.887984  98.313659    0   0.000000   0.000000    0  \n",
      "12   0.000000   0.000000   0.000000  100   0.000000   0.000000    0  \n",
      "13   0.000000   0.203666   0.000000    0  97.944664   0.518135    0  \n",
      "14   0.102881   0.040733   0.168634    0   1.660079  87.046632    0  \n",
      "15   0.000000   0.000000   0.168634    0   0.000000   0.000000  100  \n"
     ]
    }
   ],
   "source": [
    "predictions=[]\n",
    "cnf_mat =[[0 for x in range(CLASSES)] for  y in range(CLASSES)]\n",
    "for i in range(len(final_matrix)):\n",
    "    temp=[]\n",
    "    for j in range(len(final_matrix[i])):\n",
    "        if np.count_nonzero(final_matrix[i][j]) == 0 :\n",
    "            temp.append(0)\n",
    "        else:\n",
    "            tmp = np.argmax(final_matrix[i][j])\n",
    "            temp.append(tmp+1)\n",
    "            if tmp == output_image[i][j]-1:\n",
    "                cnf_mat[tmp][tmp] = cnf_mat[tmp][tmp] + 1\n",
    "            else :\n",
    "                cnf_mat[tmp][output_image[i][j]-1] = cnf_mat[tmp][output_image[i][j]-1] + 1\n",
    "    predictions.append(temp)\n",
    "for i in range(CLASSES):\n",
    "    for j in range(CLASSES):\n",
    "        cnf_mat[i][j] = 100*(float(cnf_mat[i][j])/count_mat[j])\n",
    "\n",
    "df = pd.DataFrame(cnf_mat) \n",
    "print df  \n",
    "predictions = np.array(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EsOW4bBZdtlyX4UEDHpHBYEsaenlbMveivw7btjQy2ztWcRbUMfkypguVZSkinMbx26Wh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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6de5247dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show = spectral.imshow(classes = predictions , figsize=(9,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
